- Professor:Jonathan Taylor
- Office: Sequoia Hall #137
- Phone: 723-9230,
- Office hours: Tuesday 1:30-3:30.

- TA: Bhaswar Bhattacharya
- Office: Sequoia Hall #208
- Office hours: Thursday 4:00-6:00

- TA: Yi Liu
- Office: Sequoia Hall #235
- Office hours: Wednesday 10:00-12:00

- Final exam: Following the Stanford calendar: Friday, December 14 @ 12:15PM

MW 3:15-4:30, Econ 140

- Regression Analysis by Example, Chaterjee, Hadi & Price.

An introductory statistics course, such as STATS 60.

By the end of the course, students should be able to:

- Enter tabular data using R.
- Plot data using R, to help in exploratory data analysis.
- Formulate regression models for the data, while understanding some of the limitations and assumptions implicit in using these models.
- Fit models using R and interpret the output.
- Test for associations in a given model.
- Use diagnostic plots and tests to assess the adequacy of a particular model.
- Find confidence intervals for the effects of different explanatory variables in the model.
- Use some basic model selection procedures, as found in R, to find a
bestmodel in a class of models.- Fit simple ANOVA models in R, treating them as special cases of multiple regression models.
- Fit simple logistic and Poisson regression models.

For those taking 4 units:

- 5 assignments (50%)
- data analysis project (20%)
- final exam (30%) (according to Stanford calendar: F 12/14 @ 12:15PM)

For those taking 3 units:

- 5 assignments (70%)
- final exam (30%) (according to Stanford calendar: F 12/14 @ 12:15PM)

The data analysis project description describes what is needed for your project. It is due December 7, 2012.

A complete version of the slides are available, as well as a smaller version.

Here are some review slides for the final.

- Review
- Some help for R
- Simple linear regression
- Diagnostics for simple linear regression model
- Multiple linear regression model
- Diagnostics and influence
- Interactions & ANOVA
- Transformations & Weighted Least Squares
- Correlated Errors and Whitening
- Model selection
- Logistic regression
- Poisson regression
- Penalized regression